71. The LANDSUPPORT geospatial decision support system (S-DSS) vision : operational tools to implement sustainability policies in land planning and managementFabio Terribile, Marco Acutis, Antonella Agrillo, Erlisiana Anzalone, Sayed Azam-Ali, Marialaura Bancheri, Peter Baumann, Barbara Birli, Antonello Bonfante, Marco Botta, Mitja Ferlan, Jernej Jevšenak, Primož Simončič, Mitja Skudnik, 2023, original scientific article Abstract: Nowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP-AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web-based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S-DSS LANDSUPPORT platform, consisting of a free web-based smart Geospatial CyberInfrastructure containing 15 macro-tools (and more than 100 elementary tools), co-designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web-GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling ‘on-the-fly’ in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land.
Keywords: land degradation, land management, soil, spatial decision support system, sustainability
Published in DiRROS: 13.11.2023; Views: 374; Downloads: 172
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72. Delavnica projekta LIFE SySTEMiC za lovce, lovske načrtovalce in raziskovalce v dinarsko jelovo-bukovih gozdovih, Mašun in Leskova dolina, 12. in 13. 10. 2023 : poročiloBoris Rantaša, Kristina Sever, Andrej Breznikar, Tjaša Baloh, Natalija Dovč, Evgen Ostanek, Peter Krma, Anton Smrekar, Matija Stergar, Miha Marenče, Hojka Kraigher, 2023, other monographs and other completed works Keywords: gozdovi, genetska pestrost, nega gozda, obnova gozda, upravljanje s prostoživečimi živalmi, objedanje, gozdno mladje, ujme, podnebne spremembe
Published in DiRROS: 08.11.2023; Views: 312; Downloads: 106
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73. ToF-SIMS depth profiling of metal, metal oxide, and alloy multilayers in atmospheres of ▫$H_2$▫, ▫$C_2H_2$▫, CO, and ▫$O_2$▫Jernej Ekar, Peter Panjan, Sandra Drev, Janez Kovač, 2022, original scientific article Keywords: Ions, Layers, Mass spectrometry, Metals, Oxides, SIMS depth profiling H2 C2H2 CO and O2 atmosphere gas flooding cluster secondary ions matrix effect
Published in DiRROS: 18.10.2023; Views: 329; Downloads: 148
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74. Impacts of Nature and landscape protection Act on forest management in SlovakiaKlára Báliková, Michaela Korená Hillayová, Daniel Halaj, Alex Bumbera, Peter Kicko, Jaroslav Šálka, 2023, published scientific conference contribution Keywords: forest policy, nature protection, cross-sectoral impacts, compensation payments
Published in DiRROS: 05.10.2023; Views: 398; Downloads: 102
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76. 250 let načrtnega usmerjanja razvoja gozda in 135 let usmerjanja populacij prostoživečih živalskih vrst v Trnovskem gozduEdo Kozorog, Peter Razpet, 2023, professional article Abstract: Pred 250-timi leti je bil narejen prvi gozdnogospodarski načrt za Trnovski gozd, ki je začetek načrtnega gospodarjenja z gozdovi v Sloveniji. Že ob koncu 18. stoletja so takratni gozdnogospodarski načrti vsebovali tudi podatke za lovne vrste v Trnovskem gozdu. V prispevku je predstavljen razvoj ključnih živalskih in rastlinskih vrst v Trnovskem gozdu prek kazalnikov, ki so sestavni del gozdnogospodarskih načrtov. Iz prikaza izhaja, da je bil razvoj nekaterih vrst zelo dinamičen in soodvisen, na kar se je treba pri usmerjanju razvoja stalno prilagajati. Izpostavljena je tudi težava pomanjkljivih podatkov o stanju nekaterih, zlasti ogroženih vrst ter posledično nezanesljivih ocen vzročnih povezav.
Keywords: Trnovski gozd, gozdnogospodarsko načrtovanje, upravljanje z divjadjo, ogrožene vrste, Natura 2000
Published in DiRROS: 03.10.2023; Views: 370; Downloads: 89
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79. Algorithm instance footprint : separating easily solvable and challenging problem instancesAna Nikolikj, Sašo Džeroski, Mario Andrés Muñoz, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: black-box optimization, algorithms, problem instances, machine learning
Published in DiRROS: 15.09.2023; Views: 282; Downloads: 188
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80. Assessing the generalizability of a performance predictive modelAna Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution Keywords: algorithms, predictive models, machine learning
Published in DiRROS: 15.09.2023; Views: 300; Downloads: 201
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